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Fahimi Hnazaee M, Verwoert M, Freudenburg ZV, van der Salm SMA, Aarnoutse EJ, Leinders S, Van Hulle MM, Ramsey NF, Vansteensel MJ. Towards predicting ECoG-BCI performance: assessing the potential of scalp-EEG . J Neural Eng 2022; 19:046045. [PMID: 35931055 DOI: 10.1088/1741-2552/ac8764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/05/2022] [Indexed: 11/11/2022]
Abstract
Objective. Implanted brain-computer interfaces (BCIs) employ neural signals to control a computer and may offer an alternative communication channel for people with locked-in syndrome (LIS). Promising results have been obtained using signals from the sensorimotor (SM) area. However, in earlier work on home-use of an electrocorticography (ECoG)-based BCI by people with LIS, we detected differences in ECoG-BCI performance, which were related to differences in the modulation of low frequency band (LFB) power in the SM area. For future clinical implementation of ECoG-BCIs, it will be crucial to determine whether reliable performance can be predicted before electrode implantation. To assess if non-invasive scalp-electroencephalography (EEG) could serve such prediction, we here investigated if EEG can detect the characteristics observed in the LFB modulation of ECoG signals.Approach. We included three participants with LIS of the earlier study, and a control group of 20 healthy participants. All participants performed a Rest task, and a Movement task involving actual (healthy) or attempted (LIS) hand movements, while their EEG signals were recorded.Main results.Data of the Rest task was used to determine signal-to-noise ratio, which showed a similar range for LIS and healthy participants. Using data of the Movement task, we selected seven EEG electrodes that showed a consistent movement-related decrease in beta power (13-30 Hz) across healthy participants. Within the EEG recordings of this subset of electrodes of two LIS participants, we recognized the phenomena reported earlier for the LFB in their ECoG recordings. Specifically, strong movement-related beta band suppression was observed in one, but not the other, LIS participant, and movement-related alpha band (8-12 Hz) suppression was practically absent in both. Results of the third LIS participant were inconclusive due to technical issues with the EEG recordings.Significance. Together, these findings support a potential role for scalp EEG in the presurgical assessment of ECoG-BCI candidates.
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Affiliation(s)
| | - Maxime Verwoert
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Zachary V Freudenburg
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sandra M A van der Salm
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sacha Leinders
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marc M Van Hulle
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Nick F Ramsey
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
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Fahimi Hnazaee M, Wittevrongel B, Khachatryan E, Libert A, Carrette E, Dauwe I, Meurs A, Boon P, Van Roost D, Van Hulle MM. Localization of deep brain activity with scalp and subdural EEG. Neuroimage 2020; 223:117344. [PMID: 32898677 DOI: 10.1016/j.neuroimage.2020.117344] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 07/27/2020] [Accepted: 08/31/2020] [Indexed: 01/11/2023] Open
Abstract
To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were ~70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around ~2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. SIGNIFICANCE STATEMENT: Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface.
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Affiliation(s)
| | - Benjamin Wittevrongel
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Elvira Khachatryan
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Arno Libert
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Evelien Carrette
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Ine Dauwe
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Alfred Meurs
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Paul Boon
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Dirk Van Roost
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Marc M Van Hulle
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
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Mégevand P, Hamid L, Dümpelmann M, Heers M. New horizons in clinical electric source imaging. ZEITSCHRIFT FUR EPILEPTOLOGIE 2019. [DOI: 10.1007/s10309-019-0258-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Wittevrongel B, Khachatryan E, Fahimi Hnazaee M, Camarrone F, Carrette E, De Taeye L, Meurs A, Boon P, Van Roost D, Van Hulle MM. Decoding Steady-State Visual Evoked Potentials From Electrocorticography. Front Neuroinform 2018; 12:65. [PMID: 30319386 PMCID: PMC6168710 DOI: 10.3389/fninf.2018.00065] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 09/06/2018] [Indexed: 12/02/2022] Open
Abstract
We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency- and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human subject. The paradigm is popular in EEG-based Brain Computer Interfacing where selectable targets are encoded by different frequency- and/or phase-tagged stimuli. We compare the performance of two state-of-the-art SSVEP decoders on both ECoG- and scalp-recorded EEG signals, and show that ECoG-based decoding is more accurate for very short stimulation lengths (i.e., less than 1 s). Furthermore, whereas the accuracy of scalp-EEG decoding benefits from a multi-electrode approach, to address interfering EEG responses and noise, ECoG decoding enjoys only a marginal improvement as even a single electrode, placed over the posterior part of the primary visual cortex, seems to suffice. This study shows, for the first time, that EEG-based SSVEP decoders can in principle be applied to ECoG, and can be expected to yield faster decoding speeds using less electrodes.
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Affiliation(s)
- Benjamin Wittevrongel
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Elvira Khachatryan
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Mansoureh Fahimi Hnazaee
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Flavio Camarrone
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Evelien Carrette
- Laboratory of Clinical and Experimental Neurophysiology, Neurology Department, Ghent University Hospital, Ghent, Belgium
| | - Leen De Taeye
- Laboratory of Clinical and Experimental Neurophysiology, Neurology Department, Ghent University Hospital, Ghent, Belgium
| | - Alfred Meurs
- Laboratory of Clinical and Experimental Neurophysiology, Neurology Department, Ghent University Hospital, Ghent, Belgium
| | - Paul Boon
- Laboratory of Clinical and Experimental Neurophysiology, Neurology Department, Ghent University Hospital, Ghent, Belgium
| | - Dirk Van Roost
- Department of Neurosurgery, Ghent University Hospital, Ghent, Belgium
| | - Marc M. Van Hulle
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
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Todaro C, Marzetti L, Valdés Sosa PA, Valdés-Hernandez PA, Pizzella V. Mapping Brain Activity with Electrocorticography: Resolution Properties and Robustness of Inverse Solutions. Brain Topogr 2018; 32:583-598. [PMID: 29362974 DOI: 10.1007/s10548-018-0623-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 01/16/2018] [Indexed: 10/18/2022]
Abstract
Electrocorticography (ECoG) is an electrophysiological technique that records brain activity directly from the cortical surface with high temporal (ms) and spatial (mm) resolution. Its major limitations are in the high invasiveness and in the restricted field-of-view of the electrode grid, which partially covers the cortex. To infer brain activity at locations different from just below the electrodes, it is necessary to solve the electromagnetic inverse problem. Limitations in the performance of source reconstruction algorithms from ECoG have been, to date, only partially addressed in the literature, and a systematic evaluation is still lacking. The main goal of this study is to provide a quantitative evaluation of resolution properties of widely used inverse methods (eLORETA and MNE) for various ECoG grid sizes, in terms of localization error, spatial dispersion, and overall amplitude. Additionally, this study aims at evaluating how the use of simultaneous electroencephalography (EEG) affects the above properties. For these purposes, we take advantage of a unique dataset in which a monkey underwent a simultaneous recording with a 128 channel ECoG grid and an 18 channel EEG grid. Our results show that, in general conditions, the reconstruction of cortical activity located more than 1 cm away from the ECoG grid is not accurate, since the localization error increases linearly with the distance from the electrodes. This problem can be partially overcome by recording simultaneously ECoG and EEG. However, this analysis enlightens the necessity to design inverse algorithms specifically targeted at taking into account the limited field-of-view of the ECoG grid.
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Fiederer LDJ, Lahr J, Vorwerk J, Lucka F, Aertsen A, Wolters CH, Schulze-Bonhage A, Ball T. Electrical Stimulation of the Human Cerebral Cortex by Extracranial Muscle Activity: Effect Quantification With Intracranial EEG and FEM Simulations. IEEE Trans Biomed Eng 2016; 63:2552-2563. [PMID: 27448334 PMCID: PMC5298223 DOI: 10.1109/tbme.2016.2570743] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
OBJECTIVE Electric fields (EF) of approx. 0.2 V/m have been shown to be sufficiently strong to both modulate neuronal activity in the cerebral cortex and have measurable effects on cognitive performance. We hypothesized that the EF caused by the electrical activity of extracranial muscles during natural chewing may reach similar strength in the cerebral cortex and hence might act as an endogenous modality of brain stimulation. Here, we present first steps toward validating this hypothesis. METHODS Using a realistic volume conductor head model of an epilepsy patient having undergone intracranial electrode placement and utilizing simultaneous intracranial and extracranial electrical recordings during chewing, we derive predictions about the chewing-related cortical EF strength to be expected in healthy individuals. RESULTS We find that in the region of the temporal poles, the expected EF strength may reach amplitudes in the order of 0.1-1 V/m. CONCLUSION The cortical EF caused by natural chewing could be large enough to modulate ongoing neural activity in the cerebral cortex and influence cognitive performance. SIGNIFICANCE The present study lends first support for the assumption that extracranial muscle activity might represent an endogenous source of electrical brain stimulation. This offers a new potential explanation for the puzzling effects of gum chewing on cognition, which have been repeatedly reported in the literature.
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Beniczky S, Rosenzweig I, Scherg M, Jordanov T, Lanfer B, Lantz G, Larsson PG. Ictal EEG source imaging in presurgical evaluation: High agreement between analysis methods. Seizure 2016; 43:1-5. [PMID: 27764709 PMCID: PMC5176190 DOI: 10.1016/j.seizure.2016.09.017] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 09/24/2016] [Accepted: 09/30/2016] [Indexed: 11/17/2022] Open
Abstract
There was good agreement between different methods of ictal EEG source imaging. Ictal source imaging achieved an accuracy of 73% (for operated patients: 86%). Agreement between all methods did not necessarily imply accuracy of localization.
Purpose To determine the agreement between five different methods of ictal EEG source imaging, and to assess their accuracy in presurgical evaluation of patients with focal epilepsy. It was hypothesized that high agreement between methods was associated with higher localization-accuracy. Methods EEGs were recorded with a 64-electrode array. Thirty-eight seizures from 22 patients were analyzed using five different methods phase mapping, dipole fitting, CLARA, cortical-CLARA and minimum norm. Localization accuracy was determined at sub-lobar level. Reference standard was the final decision of the multidisciplinary epilepsy surgery team, and, for the operated patients, outcome one year after surgery. Results Agreement between all methods was obtained in 13 patients (59%) and between all but one methods in additional six patients (27%). There was a trend for minimum norm being less accurate than phase mapping, but none of the comparisons reached significance. Source imaging in cases with agreement between all methods was not more accurate than in the other cases. Ictal source imaging achieved an accuracy of 73% (for operated patients: 86%). Conclusion There was good agreement between different methods of ictal source imaging. However, good inter-method agreement did not necessarily imply accurate source localization, since all methods faced the limitations of the inverse solution.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
| | - Ivana Rosenzweig
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark; Sleep and Brain Plasticity Centre, Department of Neuroimaging, IOPPN, King's College and Imperial College, London, UK
| | | | | | | | - Göran Lantz
- Clinical Neurophysiology Unit, Department of Clinical Sciences, Lund University, Lund, Sweden; Electrical Geodesics, Inc., Eugene, OR, USA
| | - Pål Gunnar Larsson
- Clinical Neurophysiology Section, Department of Neurosurgery, Oslo University Hospital, Norway
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Strobbe G, Carrette E, López JD, Montes Restrepo V, Van Roost D, Meurs A, Vonck K, Boon P, Vandenberghe S, van Mierlo P. Electrical source imaging of interictal spikes using multiple sparse volumetric priors for presurgical epileptogenic focus localization. NEUROIMAGE-CLINICAL 2016; 11:252-263. [PMID: 26958464 PMCID: PMC4773507 DOI: 10.1016/j.nicl.2016.01.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Revised: 10/09/2015] [Accepted: 01/17/2016] [Indexed: 11/07/2022]
Abstract
Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP) approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i) an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii) an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii) an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time epochs were in the same range as the LORETA and ECD techniques. We found distances smaller than 23 mm, with robust results for all the patients. For the finite difference models, we found that the distances to the resection border for the MSVP inversions of the full spike time epochs were generally smaller compared to the MSVP inversions of the time epochs before the spike peak. The results also suggest that the inversions using the finite difference models resulted in slightly smaller distances to the resection border compared to the spherical models. The results we obtained are promising because the MSVP approach allows to study the network of the estimated source-intensities and allows to characterize the spatial extent of the underlying sources. A Bayesian ESI technique is evaluated to localize interictal spike activity. Averaged spikes in six patients were used that were seizure free after surgery. We compared the technique with the LORETA an ECD technique. We evaluated both spherical and 5-layered finite difference forward models. Our approach is potentially useful to delineate the irritative zone.
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Affiliation(s)
- Gregor Strobbe
- Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium.
| | - Evelien Carrette
- Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
| | - José David López
- SISTEMIC, Department of Electronic Engineering, Universidad de Antioquia UDEA, Calle 70 No. 52-21,Medellín, Colombia.
| | - Victoria Montes Restrepo
- Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium
| | - Dirk Van Roost
- Department of Neurosurgery, Ghent University Hospital, Ghent, Belgium.
| | - Alfred Meurs
- Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
| | - Kristl Vonck
- Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
| | - Paul Boon
- Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
| | - Stefaan Vandenberghe
- Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium.
| | - Pieter van Mierlo
- Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium.
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Influence of Intracranial Electrode Density and Spatial Configuration on Interictal Spike Localization. J Clin Neurophysiol 2015; 32:e30-40. [DOI: 10.1097/wnp.0000000000000153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Influence of the head model on EEG and MEG source connectivity analyses. Neuroimage 2015; 110:60-77. [PMID: 25638756 DOI: 10.1016/j.neuroimage.2015.01.043] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Revised: 12/06/2014] [Accepted: 01/23/2015] [Indexed: 11/21/2022] Open
Abstract
The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problems. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3-layer model comprising only the skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution. Moreover, we demonstrate that the very conservative ICoh is relatively safe from the crosstalk effects caused by imperfect head models, as opposed to the GPDC.
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Hu X, Wang Y, Zhao T, Gunduz A. Neural coding for effective rehabilitation. BIOMED RESEARCH INTERNATIONAL 2014; 2014:286505. [PMID: 25258708 PMCID: PMC4167232 DOI: 10.1155/2014/286505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 07/23/2014] [Accepted: 08/10/2014] [Indexed: 01/31/2023]
Abstract
Successful neurological rehabilitation depends on accurate diagnosis, effective treatment, and quantitative evaluation. Neural coding, a technology for interpretation of functional and structural information of the nervous system, has contributed to the advancements in neuroimaging, brain-machine interface (BMI), and design of training devices for rehabilitation purposes. In this review, we summarized the latest breakthroughs in neuroimaging from microscale to macroscale levels with potential diagnostic applications for rehabilitation. We also reviewed the achievements in electrocorticography (ECoG) coding with both animal models and human beings for BMI design, electromyography (EMG) interpretation for interaction with external robotic systems, and robot-assisted quantitative evaluation on the progress of rehabilitation programs. Future rehabilitation would be more home-based, automatic, and self-served by patients. Further investigations and breakthroughs are mainly needed in aspects of improving the computational efficiency in neuroimaging and multichannel ECoG by selection of localized neuroinformatics, validation of the effectiveness in BMI guided rehabilitation programs, and simplification of the system operation in training devices.
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Affiliation(s)
- Xiaoling Hu
- Interdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
| | - Yiwen Wang
- Qiushi Academy for Advanced Studies, Zhejiang University, Zhejiang 310027, China
| | - Ting Zhao
- Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, VA 20147, USA
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
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Aydin Ü, Vorwerk J, Küpper P, Heers M, Kugel H, Galka A, Hamid L, Wellmer J, Kellinghaus C, Rampp S, Wolters CH. Combining EEG and MEG for the reconstruction of epileptic activity using a calibrated realistic volume conductor model. PLoS One 2014; 9:e93154. [PMID: 24671208 PMCID: PMC3966892 DOI: 10.1371/journal.pone.0093154] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Accepted: 02/28/2014] [Indexed: 11/18/2022] Open
Abstract
To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data.
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Affiliation(s)
- Ümit Aydin
- Institute for Biomagnetism and Biosignalanalysis, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Johannes Vorwerk
- Institute for Biomagnetism and Biosignalanalysis, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Philipp Küpper
- Institute for Biomagnetism and Biosignalanalysis, Westfälische Wilhelms-Universität Münster, Münster, Germany
- Department of Neurology, Klinikum Osnabrück, Osnabrück, Germany
| | - Marcel Heers
- Ruhr-Epileptology Department of Neurology, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - Harald Kugel
- Department of Clinical Radiology, Universitätsklinikum Münster, Münster, Germany
| | - Andreas Galka
- Department of Neuropediatrics, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Laith Hamid
- Department of Neuropediatrics, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Jörg Wellmer
- Ruhr-Epileptology Department of Neurology, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | | | - Stefan Rampp
- Epilepsy Center, Department of Neurology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Carsten Hermann Wolters
- Institute for Biomagnetism and Biosignalanalysis, Westfälische Wilhelms-Universität Münster, Münster, Germany
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13
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Ramantani G, Dümpelmann M, Koessler L, Brandt A, Cosandier-Rimélé D, Zentner J, Schulze-Bonhage A, Maillard LG. Simultaneous subdural and scalp EEG correlates of frontal lobe epileptic sources. Epilepsia 2014; 55:278-88. [DOI: 10.1111/epi.12512] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2013] [Indexed: 11/26/2022]
Affiliation(s)
| | | | - Laurent Koessler
- Research Center for Automatic Control (CRAN); University of Lorraine; CNRS; UMR 7039; Vandoeuvre France
| | - Armin Brandt
- Epilepsy Center; University Hospital Freiburg; Freiburg Germany
| | | | - Josef Zentner
- Department of Neurosurgery; University Hospital Freiburg; Freiburg Germany
| | | | - Louis Georges Maillard
- Research Center for Automatic Control (CRAN); University of Lorraine; CNRS; UMR 7039; Vandoeuvre France
- Department of Neurology, Central University Hospital; CHU de Nancy; Nancy France
- Medical Faculty; University of Lorraine; Nancy France
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14
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Scalp EEG is not a Blur: It Can See High Frequency Oscillations Although Their Generators are Small. Brain Topogr 2013; 27:683-704. [DOI: 10.1007/s10548-013-0321-y] [Citation(s) in RCA: 102] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2013] [Accepted: 09/27/2013] [Indexed: 11/30/2022]
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